No abstract

Benzodiazepines is a class of medications frequently prescribed to patients with post-traumatic stress disorder. Patients with PTSD have a notable increased risk of suicide compared to the general population. These medications have been theorized to increase suicidality and pose a risk when used in this patient population. Previous research has found little utility of using benzodiazepines in the PTSD population. However, benzodiazepines are still commonly prescribed by some clinicians for their symptomatic benefit. This study aims to identify the comparative efficacy of commonly prescribed benzodiazepines including midazolam, lorazepam, alprazolam, clonazepam, diazepam and temazepam in relation to suicide-related behaviors (SRBs). A total of 38,807 patients who had an ICD9 or ICD10 diagnosis of PTSD from January 2004 to October 2019 were identified through an electronic medical record database. Inclusion criteria include patients that initiated one of the above benzodiazepines after PTSD diagnosis. Exclusion criteria include previous history of benzodiazepine usage or history of SRBs within the last year prior to enrollment. For patients enrolled in this study, other concomitant drugs were not limited. The primary outcome was onset of SRBs with each respective benzodiazepine. SRBs were identified as ideation, attempt, or death from suicide. We emulated clinical trials of head-to-head comparison between two drugs by pooled logistic regression methods with the Firth option adjusting for baseline characteristics and post-baseline confounders. A total of 5753 patients were eligible for this study, with an average follow up of 5.82 months. The overall incidence for SRB was 1.51% (87/5753). Head-to-head comparisons identified that patients who received alprazolam had fewer SRBs compared to clonazepam (p = 0.0351) and lorazepam (p = 0.0373), and patients taking midazolam experienced fewer relative incidences of SRBs when compared to lorazepam (p = 0.0021) and clonazepam (p = 0.0297). After adjusting for the false discovery rate (FDR), midazolam still had fewer SRBs compared to lorazepam (FDR-adjusted p value = 0.0315). Certain benzodiazepines may provide a reduced risk of development of SRBs, suggesting careful consideration when prescribing benzodiazepines to the PTSD population.

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Aripiprazole, Levomilnacipran, Sertraline, Tramadol, Fentanyl, or Fluoxetine, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, were strong indicators for no SREs within one year. The use of Trazodone and Citalopram at baseline predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making.

Post-traumatic stress disorder (PTSD) is a prevalent mental disorder marked by psychological and behavioral changes. Currently, there is no consensus of preferred antipsychotics to be used for the treatment of PTSD. We aim to discover whether certain antipsychotics have decreased suicide risk in the PTSD population, as these patients may be at higher risk. A total of 38,807 patients were identified with a diagnosis of PTSD through the ICD9 or ICD10 codes from January 2004 to October 2019. An emulation of randomized clinical trials was conducted to compare the outcomes of suicide-related events (SREs) among PTSD patients who ever used one of eight individual antipsychotics after the diagnosis of PTSD. Exclusion criteria included patients with a history of SREs and a previous history of antipsychotic use within one year before enrollment. Eligible individuals were assigned to a treatment group according to the antipsychotic initiated and followed until stopping current treatment, switching to another same class of drugs, death, or loss to follow up. The primary outcome was to identify the frequency of SREs associated with each antipsychotic. SREs were defined as ideation, attempts, and death by suicide. Pooled logistic regression methods with the Firth option were conducted to compare two drugs for their outcomes using SAS version 9.4 (SAS Institute, Cary, NC, USA). The results were adjusted for baseline characteristics and post-baseline, time-varying confounders. A total of 5294 patients were eligible for enrollment with an average follow up of 7.86 months. A total of 157 SREs were recorded throughout this study. Lurasidone showed a statistically significant decrease in SREs when compared head to head to almost all the other antipsychotics: aripiprazole, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone (p < 0.0001 and false discovery rate-adjusted p value < 0.0004). In addition, olanzapine was associated with higher SREs than quetiapine and risperidone, and ziprasidone was associated with higher SREs than risperidone. The results of this study suggest that certain antipsychotics may put individuals within the PTSD population at an increased risk of SREs, and that careful consideration may need to be taken when prescribed.

A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug–disease associations through the analysis of the similarity of GES profiles on known pairs of drug–disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug–disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway.